Systems driven intelligent decision support methods for ship collision and grounding prevention: Present status, possible solutions, and challenges

Despite advancements in science and technology, ship collisions and groundings remain the most prevalent types of maritime accidents. Recent developments in accident prevention and mitigation methods have been bolstered by the rise of autonomous shipping, digital technologies, and Artificial Intelligence (AI). This paper provides an exhaustive review of the characteristics of fleets at risk over the past two decades, emphasizing the societal impacts of preventing collisions and groundings. It also delves into the key components of decision support systems from a ship's perspective and undertakes a systematic literature review on the foundations and applications of systems-driven decision support methods for ship collision and grounding prevention. The study covers risk analysis, damage evaluation, and ship motion prediction methods from 2002 to 2023. The conclusions indicate that modern ship science methods are increasingly valuable in ship design and maritime operations. Emerging multi-physics systems and AI-enabled predictive analytics show potential for future integration into intelligent decision support systems. The strategic research challenges include (1) underestimating the impacts of real operational conditions on ship safety, (2) the inherent limitations of static risk analysis and finite numerical methods, and (3) the need for rapid, probabilistic assessments of damage extents. The demands and trends suggest that leveraging big data analytics and rapid prediction methods, underpinned by digitalization and AI technologies, represents the most feasible way forward.

• Record URL:
  • https://doi.org/10.1016/j.ress.2024.110489
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0951832024005611
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09518320
• Supplemental Notes:
  • © 2024 The Authors. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhang, Mingyang
  • 0000-0001-5820-2789
  • Taimuri, Ghalib
  • Zhang, Jinfen
  • 0000-0003-2703-6663
  • Zhang, Di
  • 0000-0001-8790-9206
  • Yan, Xinping
  • Kujala, Pentti
  • Hirdaris, Spyros
  • 0000-0002-4700-6325
• Publication Date: 2025-1

Language

• English

Media Info

• Media Type: Digital/other
• Features: Appendices; Figures; Maps; References; Tables;
• Pagination: 110489
• Serial:
  • Reliability Engineering & System Safety
  • Volume: 253
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0951-8320
  • Serial URL: https://www.sciencedirect.com/journal/reliability-engineering-and-system-safety

Subject/Index Terms

• TRT Terms: Crash data; Decision support systems; Groundings (Maritime crashes); Maritime safety; Prevention; Water transportation crashes
• Subject Areas: Marine Transportation; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01934489
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 21 2024 8:46AM